Want to get hired at UM IT Solutions?

Machine Learning Intern

UM IT Solutions

HybridHybrid

Original Job Summary

About Machine Learning Intern

Unified Mentor provides students and graduates with hands-on learning opportunities and career growth in Machine Learning and Data Science.

Role Overview

As a Machine Learning Intern, you will work on real-world projects, enhancing your practical skills in data analysis and model development.

Responsibilities

  • Design, test, and optimize machine learning models
  • Analyze and preprocess datasets
  • Develop algorithms and predictive models
  • Use tools like TensorFlow, PyTorch, and Scikit-learn
  • Document findings and create reports

Requirements

  • Enrolled in or a graduate of a relevant program (Computer Science, AI, Data Science, or related field)
  • Knowledge of machine learning concepts and algorithms
  • Proficiency in Python or R (preferred)
  • Strong analytical and teamwork skills

Benefits

  • Stipend: ₹7,500 - ₹15,000 (Performance-Based)
  • Hands-on machine learning experience
  • Internship Certificate & Letter of Recommendation
  • Real-world project contributions for your portfolio

Equal Opportunity

Unified Mentor is an equal-opportunity employer, welcoming candidates from all backgrounds.

Key skills/competency

  • Machine Learning
  • Data Science
  • Python
  • Model Development
  • Data Analysis
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Algorithm Design
  • Teamwork

How to Get Hired at UM IT Solutions

🎯 Tips for Getting Hired

  • Research Unified Mentor's culture: Study mission, values, and recent news.
  • Customize your resume: Emphasize machine learning and data skills.
  • Highlight project experience: Include real-world model work and analyses.
  • Prepare for interviews: Review technical questions and teamwork scenarios.

📝 Interview Preparation Advice

Technical Preparation

Review Python libraries and ML frameworks.
Practice TensorFlow, PyTorch coding challenges.
Work on data preprocessing and model testing.
Study algorithm optimization and evaluation metrics.

Behavioral Questions

Explain teamwork in project scenarios.
Demonstrate problem-solving using past examples.
Discuss handling project feedback effectively.
Show adaptability during learning challenges.